In [23]:
import pandas as pd
%matplotlib inline

data_train = pd.read_csv('train.csv')  
data_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
In [24]:
data_train.describe()
Out[24]:
PassengerId Survived Pclass Age SibSp Parch Fare
count 891.000000 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208
std 257.353842 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429
min 1.000000 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400
50% 446.000000 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200
75% 668.500000 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200
In [25]:
data_train.head()
Out[25]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
In [26]:
data_test = pd.read_csv('test.csv')  
data_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
PassengerId    418 non-null int64
Pclass         418 non-null int64
Name           418 non-null object
Sex            418 non-null object
Age            332 non-null float64
SibSp          418 non-null int64
Parch          418 non-null int64
Ticket         418 non-null object
Fare           417 non-null float64
Cabin          91 non-null object
Embarked       418 non-null object
dtypes: float64(2), int64(4), object(5)
memory usage: 36.0+ KB
In [27]:
data_test.head()

# data_train.drop(['Ticket', 'Cabin'], axis = 1)
# data_train.dropna()
Out[27]:
PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S
In [30]:
#coding:utf-8

import matplotlib.pyplot as plt

# 用来正常显示中文标签
plt.rcParams['font.sans-serif'] = ['SimHei'] 
# 用来正常显示负号
plt.rcParams['axes.unicode_minus'] = False 
fig = plt.figure(figsize=(18,6), dpi=1600)
# 设定图表颜色alpha参数
fig.set(alpha=0.2)  

# 在一张大图里分列几个小图
plt.subplot2grid((2,3),(0,0))             
data_train.Survived.value_counts().plot(kind='bar')# plots a bar graph of those who surived vs those who did not. 
plt.title(u"获救情况 (1为获救)") 
plt.ylabel(u"人数")  

plt.subplot2grid((2,3),(0,1))
data_train.Pclass.value_counts().plot(kind="bar")
plt.ylabel(u"人数")
plt.title(u"乘客等级分布")

plt.subplot2grid((2,3),(0,2))
plt.scatter(data_train.Survived, data_train.Age)
plt.ylabel(u"年龄")                         
plt.grid(b=True, which='major', axis='y') 
plt.title(u"按年龄看获救分布 (1为获救)")

plt.subplot2grid((2,3),(1,0), colspan=2)
data_train.Age[data_train.Pclass == 1].plot(kind='kde')   
data_train.Age[data_train.Pclass == 2].plot(kind='kde')
data_train.Age[data_train.Pclass == 3].plot(kind='kde')
plt.xlabel(u"年龄")
plt.ylabel(u"密度") 
plt.title(u"各等级的乘客年龄分布")
plt.legend((u'头等舱', u'2等舱',u'3等舱'),loc='best') 

plt.subplot2grid((2,3),(1,2))
data_train.Embarked.value_counts().plot(kind='bar')
plt.title(u"各登船口岸上船人数")
plt.ylabel(u"人数")  
plt.show()
In [31]:
# 各乘客等级的获救情况
fig = plt.figure()
fig.set(alpha=0.2)  # 设定图表颜色alpha参数

Survived_0 = data_train.Pclass[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Pclass[data_train.Survived == 1].value_counts()
df = pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u"各乘客等级的获救情况")
plt.xlabel(u"乘客等级") 
plt.ylabel(u"人数") 

plt.show()

<matplotlib.figure.Figure at 0x12c02518>
In [32]:
# 各登录港口的获救情况
fig = plt.figure()
fig.set(alpha=0.2)  # 设定图表颜色alpha参数

Survived_0 = data_train.Embarked[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Embarked[data_train.Survived == 1].value_counts()
df = pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u"各登录港口乘客的获救情况")
plt.xlabel(u"登录港口") 
plt.ylabel(u"人数") 

plt.show()

<matplotlib.figure.Figure at 0x86affd0>
In [34]:
# 各性别的获救情况
fig = plt.figure()
fig.set(alpha=0.2)  # 设定图表颜色alpha参数

Survived_m = data_train.Survived[data_train.Sex == 'male'].value_counts()
Survived_f = data_train.Survived[data_train.Sex == 'female'].value_counts()
df = pd.DataFrame({u'男性':Survived_m, u'女性':Survived_f})
df.plot(kind='bar', stacked=True)
plt.title(u"获救情况(1=获救)")
plt.xlabel(u"性别") 
plt.ylabel(u"人数")
plt.show()

<matplotlib.figure.Figure at 0x12dc46a0>
In [42]:
# 各种舱级别情况下各性别的获救情况
fig = plt.figure(figsize=(18,4), dpi=1600)
fig.set(alpha=0.2) 
plt.title(u"根据舱等级和性别的获救情况")

ax1 = fig.add_subplot(141)
female_highclass = data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts()
female_highclass = female_highclass.plot(kind='bar', label=u"女性/高级舱", color='#FA2479')
ax1.set_xticklabels([u"获救", u"未获救"], rotation=0)
plt.legend(loc='best')

ax2 = fig.add_subplot(142, sharey=ax1)
female_lowclass = data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts()
female_lowclass = female_lowclass.plot(kind='bar', label=u"女性/低级舱", color='pink')
ax2.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend(loc='best')

ax3 = fig.add_subplot(143, sharey=ax1)
male_lowclass = data_train.Survived[data_train.Sex == 'male'][data_train.Pclass != 3].value_counts()
male_lowclass = male_lowclass.plot(kind='bar', label=u"男性/高级舱",color='lightblue')
ax3.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend(loc='best')

ax4 = fig.add_subplot(144, sharey=ax1)
male_highclass = data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts()
male_highclass = male_highclass.plot(kind='bar', label=u"男性/低级舱", color='steelblue')
ax4.set_xticklabels([u"未获救", u"获救"], rotation=0)
plt.legend(loc='best')

plt.show()
In [47]:
group = data_train.groupby(['SibSp','Survived'])
df = pd.DataFrame(group.count()['PassengerId'])
df.plot(kind='bar', stacked=True)
Out[47]:
<matplotlib.axes._subplots.AxesSubplot at 0x13005f28>
In [48]:
group = data_train.groupby(['Parch','Survived'])
df = pd.DataFrame(group.count()['PassengerId'])
df.plot(kind='bar')
Out[48]:
<matplotlib.axes._subplots.AxesSubplot at 0x13173b70>
In [52]:
X_train = data_train.drop("Survived",axis=1)
Y_train = data_train["Survived"]
X_test  = data_test.drop("PassengerId",axis=1).copy()
# X_test  = data_test.drop("PassengerId",axis=1)
In [54]:
data_train.describe(include=['O'])
Out[54]:
Name Sex Ticket Cabin Embarked
count 891 891 891 204 889
unique 891 2 681 147 3
top Strom, Mrs. Wilhelm (Elna Matilda Persson) male 347082 C23 C25 C27 S
freq 1 577 7 4 644
In [55]:
data_train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)
Out[55]:
Pclass Survived
0 1 0.629630
1 2 0.472826
2 3 0.242363
In [56]:
data_train[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)
Out[56]:
Sex Survived
0 female 0.742038
1 male 0.188908
In [57]:
data_train[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean()
Out[57]:
SibSp Survived
0 0 0.345395
1 1 0.535885
2 2 0.464286
3 3 0.250000
4 4 0.166667
5 5 0.000000
6 8 0.000000
In [60]:
data_train[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived')
Out[60]:
Parch Survived
4 4 0.000000
6 6 0.000000
5 5 0.200000
0 0 0.343658
2 2 0.500000
1 1 0.550847
3 3 0.600000
In [62]:
data_train[['Age', 'Survived']].groupby(['Age'], as_index=False).mean()
Out[62]:
Age Survived
0 0.42 1.000000
1 0.67 1.000000
2 0.75 1.000000
3 0.83 1.000000
4 0.92 1.000000
5 1.00 0.714286
6 2.00 0.300000
7 3.00 0.833333
8 4.00 0.700000
9 5.00 1.000000
10 6.00 0.666667
11 7.00 0.333333
12 8.00 0.500000
13 9.00 0.250000
14 10.00 0.000000
15 11.00 0.250000
16 12.00 1.000000
17 13.00 1.000000
18 14.00 0.500000
19 14.50 0.000000
20 15.00 0.800000
21 16.00 0.352941
22 17.00 0.461538
23 18.00 0.346154
24 19.00 0.360000
25 20.00 0.200000
26 20.50 0.000000
27 21.00 0.208333
28 22.00 0.407407
29 23.00 0.333333
... ... ...
58 44.00 0.333333
59 45.00 0.416667
60 45.50 0.000000
61 46.00 0.000000
62 47.00 0.111111
63 48.00 0.666667
64 49.00 0.666667
65 50.00 0.500000
66 51.00 0.285714
67 52.00 0.500000
68 53.00 1.000000
69 54.00 0.375000
70 55.00 0.500000
71 55.50 0.000000
72 56.00 0.500000
73 57.00 0.000000
74 58.00 0.600000
75 59.00 0.000000
76 60.00 0.500000
77 61.00 0.000000
78 62.00 0.500000
79 63.00 1.000000
80 64.00 0.000000
81 65.00 0.000000
82 66.00 0.000000
83 70.00 0.000000
84 70.50 0.000000
85 71.00 0.000000
86 74.00 0.000000
87 80.00 1.000000

88 rows × 2 columns

In [67]:
fig = plt.figure(figsize=(18,6), dpi=1600)
fig.set(alpha=0.2)  

Survived_0 = data_train.Age[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Age[data_train.Survived == 1].value_counts()
df=pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u"各年龄段的获救情况")
plt.xlabel(u"年龄") 
plt.ylabel(u"人数") 

plt.show()

<matplotlib.figure.Figure at 0x12d1bc18>
In [71]:
data_train = data_train.drop(['Ticket', 'Cabin'], axis=1)
data_train.shape
Out[71]:
(891, 10)
In [73]:
data_test.shape
Out[73]:
(418, 11)
In [74]:
data_test = data_test.drop(['Ticket', 'Cabin'], axis=1)
data_test.shape
Out[74]:
(418, 9)
In [77]:
data_train = data_train.drop(['Name', 'PassengerId'], axis=1)
data_test = data_test.drop(['Name'], axis=1)
data_test.shape
Out[77]:
(418, 8)
In [80]:
data_train["Embarked"] = data_train["Embarked"].fillna("S")
In [84]:
data_train['Embarked'] = data_train['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)
In [85]:
data_train.head()
Out[85]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 male 22.0 1 0 7.2500 0
1 1 1 female 38.0 1 0 71.2833 1
2 1 3 female 26.0 0 0 7.9250 0
3 1 1 female 35.0 1 0 53.1000 0
4 0 3 male 35.0 0 0 8.0500 0
In [86]:
data_train['Sex'] = data_train['Sex'].map( {'female': 1, 'male': 0} ).astype(int)
In [87]:
data_train.head()
Out[87]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 0 22.0 1 0 7.2500 0
1 1 1 1 38.0 1 0 71.2833 1
2 1 3 1 26.0 0 0 7.9250 0
3 1 1 1 35.0 1 0 53.1000 0
4 0 3 0 35.0 0 0 8.0500 0
In [89]:
X_train = data_train.drop("Survived", axis=1)
Y_train = data_train["Survived"]
X_test  = data_test.drop("PassengerId", axis=1).copy()
data_train.shape
Out[89]:
(891, 8)
In [93]:
data_train
Out[93]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 0 22.0 1 0 7.2500 0
1 1 1 1 38.0 1 0 71.2833 1
2 1 3 1 26.0 0 0 7.9250 0
3 1 1 1 35.0 1 0 53.1000 0
4 0 3 0 35.0 0 0 8.0500 0
5 0 3 0 NaN 0 0 8.4583 2
6 0 1 0 54.0 0 0 51.8625 0
7 0 3 0 2.0 3 1 21.0750 0
8 1 3 1 27.0 0 2 11.1333 0
9 1 2 1 14.0 1 0 30.0708 1
10 1 3 1 4.0 1 1 16.7000 0
11 1 1 1 58.0 0 0 26.5500 0
12 0 3 0 20.0 0 0 8.0500 0
13 0 3 0 39.0 1 5 31.2750 0
14 0 3 1 14.0 0 0 7.8542 0
15 1 2 1 55.0 0 0 16.0000 0
16 0 3 0 2.0 4 1 29.1250 2
17 1 2 0 NaN 0 0 13.0000 0
18 0 3 1 31.0 1 0 18.0000 0
19 1 3 1 NaN 0 0 7.2250 1
20 0 2 0 35.0 0 0 26.0000 0
21 1 2 0 34.0 0 0 13.0000 0
22 1 3 1 15.0 0 0 8.0292 2
23 1 1 0 28.0 0 0 35.5000 0
24 0 3 1 8.0 3 1 21.0750 0
25 1 3 1 38.0 1 5 31.3875 0
26 0 3 0 NaN 0 0 7.2250 1
27 0 1 0 19.0 3 2 263.0000 0
28 1 3 1 NaN 0 0 7.8792 2
29 0 3 0 NaN 0 0 7.8958 0
... ... ... ... ... ... ... ... ...
861 0 2 0 21.0 1 0 11.5000 0
862 1 1 1 48.0 0 0 25.9292 0
863 0 3 1 NaN 8 2 69.5500 0
864 0 2 0 24.0 0 0 13.0000 0
865 1 2 1 42.0 0 0 13.0000 0
866 1 2 1 27.0 1 0 13.8583 1
867 0 1 0 31.0 0 0 50.4958 0
868 0 3 0 NaN 0 0 9.5000 0
869 1 3 0 4.0 1 1 11.1333 0
870 0 3 0 26.0 0 0 7.8958 0
871 1 1 1 47.0 1 1 52.5542 0
872 0 1 0 33.0 0 0 5.0000 0
873 0 3 0 47.0 0 0 9.0000 0
874 1 2 1 28.0 1 0 24.0000 1
875 1 3 1 15.0 0 0 7.2250 1
876 0 3 0 20.0 0 0 9.8458 0
877 0 3 0 19.0 0 0 7.8958 0
878 0 3 0 NaN 0 0 7.8958 0
879 1 1 1 56.0 0 1 83.1583 1
880 1 2 1 25.0 0 1 26.0000 0
881 0 3 0 33.0 0 0 7.8958 0
882 0 3 1 22.0 0 0 10.5167 0
883 0 2 0 28.0 0 0 10.5000 0
884 0 3 0 25.0 0 0 7.0500 0
885 0 3 1 39.0 0 5 29.1250 2
886 0 2 0 27.0 0 0 13.0000 0
887 1 1 1 19.0 0 0 30.0000 0
888 0 3 1 NaN 1 2 23.4500 0
889 1 1 0 26.0 0 0 30.0000 1
890 0 3 0 32.0 0 0 7.7500 2

891 rows × 8 columns

In [95]:
data_test['Embarked'] = data_test['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)
data_test['Sex'] = data_test['Sex'].map( {'female': 1, 'male': 0} ).astype(int)
In [98]:
data_test.head()
Out[98]:
PassengerId Pclass Sex Age SibSp Parch Fare Embarked
0 892 3 0 34.5 0 0 7.8292 2
1 893 3 1 47.0 1 0 7.0000 0
2 894 2 0 62.0 0 0 9.6875 2
3 895 3 0 27.0 0 0 8.6625 0
4 896 3 1 22.0 1 1 12.2875 0
In [101]:
import numpy as np  
guess_ages = np.zeros((2,3))
In [112]:
for i in range(0, 2):  
    for j in range(0, 3):  
        guess_median = data_train[(data_train['Sex'] == i) & \
                              (data_train['Pclass'] == j+1)]['Age'].dropna().median()
        guess_ages[i,j] = int( guess_median/0.5 + 0.5 ) * 0.5

guess_ages
Out[112]:
array([[ 40.,  30.,  25.],
       [ 35.,  28.,  21.]])
In [113]:
for i in range(0, 2):  
    for j in range(0, 3):  
        data_train.loc[ (data_train.Age.isnull()) & (data_train.Sex == i) & (data_train.Pclass == j+1), 'Age'] = guess_ages[i,j]  

data_train['Age'] = data_train['Age'].astype(int)        
# data_train[ data_train['Age'].isnull() ][['Sex','Pclass','Age']].head(10)
In [119]:
data_train
Out[119]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 0 22 1 0 7.2500 0
1 1 1 1 38 1 0 71.2833 1
2 1 3 1 26 0 0 7.9250 0
3 1 1 1 35 1 0 53.1000 0
4 0 3 0 35 0 0 8.0500 0
5 0 3 0 25 0 0 8.4583 2
6 0 1 0 54 0 0 51.8625 0
7 0 3 0 2 3 1 21.0750 0
8 1 3 1 27 0 2 11.1333 0
9 1 2 1 14 1 0 30.0708 1
10 1 3 1 4 1 1 16.7000 0
11 1 1 1 58 0 0 26.5500 0
12 0 3 0 20 0 0 8.0500 0
13 0 3 0 39 1 5 31.2750 0
14 0 3 1 14 0 0 7.8542 0
15 1 2 1 55 0 0 16.0000 0
16 0 3 0 2 4 1 29.1250 2
17 1 2 0 30 0 0 13.0000 0
18 0 3 1 31 1 0 18.0000 0
19 1 3 1 21 0 0 7.2250 1
20 0 2 0 35 0 0 26.0000 0
21 1 2 0 34 0 0 13.0000 0
22 1 3 1 15 0 0 8.0292 2
23 1 1 0 28 0 0 35.5000 0
24 0 3 1 8 3 1 21.0750 0
25 1 3 1 38 1 5 31.3875 0
26 0 3 0 25 0 0 7.2250 1
27 0 1 0 19 3 2 263.0000 0
28 1 3 1 21 0 0 7.8792 2
29 0 3 0 25 0 0 7.8958 0
... ... ... ... ... ... ... ... ...
861 0 2 0 21 1 0 11.5000 0
862 1 1 1 48 0 0 25.9292 0
863 0 3 1 21 8 2 69.5500 0
864 0 2 0 24 0 0 13.0000 0
865 1 2 1 42 0 0 13.0000 0
866 1 2 1 27 1 0 13.8583 1
867 0 1 0 31 0 0 50.4958 0
868 0 3 0 25 0 0 9.5000 0
869 1 3 0 4 1 1 11.1333 0
870 0 3 0 26 0 0 7.8958 0
871 1 1 1 47 1 1 52.5542 0
872 0 1 0 33 0 0 5.0000 0
873 0 3 0 47 0 0 9.0000 0
874 1 2 1 28 1 0 24.0000 1
875 1 3 1 15 0 0 7.2250 1
876 0 3 0 20 0 0 9.8458 0
877 0 3 0 19 0 0 7.8958 0
878 0 3 0 25 0 0 7.8958 0
879 1 1 1 56 0 1 83.1583 1
880 1 2 1 25 0 1 26.0000 0
881 0 3 0 33 0 0 7.8958 0
882 0 3 1 22 0 0 10.5167 0
883 0 2 0 28 0 0 10.5000 0
884 0 3 0 25 0 0 7.0500 0
885 0 3 1 39 0 5 29.1250 2
886 0 2 0 27 0 0 13.0000 0
887 1 1 1 19 0 0 30.0000 0
888 0 3 1 21 1 2 23.4500 0
889 1 1 0 26 0 0 30.0000 1
890 0 3 0 32 0 0 7.7500 2

891 rows × 8 columns

In [122]:
guess_test_ages = np.zeros((2,3))
for i in range(0, 2):  
    for j in range(0, 3):  
        guess_test_median = data_test[(data_test['Sex'] == i) & \
                              (data_test['Pclass'] == j+1)]['Age'].dropna().median()
        guess_test_ages[i,j] = int( guess_test_median/0.5 + 0.5 ) * 0.5
        
guess_test_ages
Out[122]:
array([[ 42.,  28.,  24.],
       [ 41.,  24.,  22.]])
In [123]:
for i in range(0, 2):  
    for j in range(0, 3):  
        data_test.loc[ (data_test.Age.isnull()) & (data_test.Sex == i) & (data_test.Pclass == j+1), 'Age'] = guess_test_ages[i,j]  

data_test['Age'] = data_test['Age'].astype(int)
In [125]:
data_test
Out[125]:
PassengerId Pclass Sex Age SibSp Parch Fare Embarked
0 892 3 0 34 0 0 7.8292 2
1 893 3 1 47 1 0 7.0000 0
2 894 2 0 62 0 0 9.6875 2
3 895 3 0 27 0 0 8.6625 0
4 896 3 1 22 1 1 12.2875 0
5 897 3 0 14 0 0 9.2250 0
6 898 3 1 30 0 0 7.6292 2
7 899 2 0 26 1 1 29.0000 0
8 900 3 1 18 0 0 7.2292 1
9 901 3 0 21 2 0 24.1500 0
10 902 3 0 24 0 0 7.8958 0
11 903 1 0 46 0 0 26.0000 0
12 904 1 1 23 1 0 82.2667 0
13 905 2 0 63 1 0 26.0000 0
14 906 1 1 47 1 0 61.1750 0
15 907 2 1 24 1 0 27.7208 1
16 908 2 0 35 0 0 12.3500 2
17 909 3 0 21 0 0 7.2250 1
18 910 3 1 27 1 0 7.9250 0
19 911 3 1 45 0 0 7.2250 1
20 912 1 0 55 1 0 59.4000 1
21 913 3 0 9 0 1 3.1708 0
22 914 1 1 41 0 0 31.6833 0
23 915 1 0 21 0 1 61.3792 1
24 916 1 1 48 1 3 262.3750 1
25 917 3 0 50 1 0 14.5000 0
26 918 1 1 22 0 1 61.9792 1
27 919 3 0 22 0 0 7.2250 1
28 920 1 0 41 0 0 30.5000 0
29 921 3 0 24 2 0 21.6792 1
... ... ... ... ... ... ... ... ...
388 1280 3 0 21 0 0 7.7500 2
389 1281 3 0 6 3 1 21.0750 0
390 1282 1 0 23 0 0 93.5000 0
391 1283 1 1 51 0 1 39.4000 0
392 1284 3 0 13 0 2 20.2500 0
393 1285 2 0 47 0 0 10.5000 0
394 1286 3 0 29 3 1 22.0250 0
395 1287 1 1 18 1 0 60.0000 0
396 1288 3 0 24 0 0 7.2500 2
397 1289 1 1 48 1 1 79.2000 1
398 1290 3 0 22 0 0 7.7750 0
399 1291 3 0 31 0 0 7.7333 2
400 1292 1 1 30 0 0 164.8667 0
401 1293 2 0 38 1 0 21.0000 0
402 1294 1 1 22 0 1 59.4000 1
403 1295 1 0 17 0 0 47.1000 0
404 1296 1 0 43 1 0 27.7208 1
405 1297 2 0 20 0 0 13.8625 1
406 1298 2 0 23 1 0 10.5000 0
407 1299 1 0 50 1 1 211.5000 1
408 1300 3 1 22 0 0 7.7208 2
409 1301 3 1 3 1 1 13.7750 0
410 1302 3 1 22 0 0 7.7500 2
411 1303 1 1 37 1 0 90.0000 2
412 1304 3 1 28 0 0 7.7750 0
413 1305 3 0 24 0 0 8.0500 0
414 1306 1 1 39 0 0 108.9000 1
415 1307 3 0 38 0 0 7.2500 0
416 1308 3 0 24 0 0 8.0500 0
417 1309 3 0 24 1 1 22.3583 1

418 rows × 8 columns

In [127]:
X_train = data_train.drop("Survived", axis=1)
Y_train = data_train["Survived"]
X_test  = data_test.drop("PassengerId", axis=1).copy()
In [132]:
X_train.describe()
Out[132]:
Pclass Sex Age SibSp Parch Fare Embarked
count 891.000000 891.000000 891.000000 891.000000 891.000000 891.000000 891.000000
mean 2.308642 0.352413 29.072952 0.523008 0.381594 32.204208 0.361392
std 0.836071 0.477990 13.326339 1.102743 0.806057 49.693429 0.635673
min 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2.000000 0.000000 21.000000 0.000000 0.000000 7.910400 0.000000
50% 3.000000 0.000000 26.000000 0.000000 0.000000 14.454200 0.000000
75% 3.000000 1.000000 36.000000 1.000000 0.000000 31.000000 1.000000
max 3.000000 1.000000 80.000000 8.000000 6.000000 512.329200 2.000000
In [135]:
data_train.loc[ data_train['Fare'] <= 7.91, 'Fare'] = 0
data_train.loc[(data_train['Fare'] > 7.91) & (data_train['Fare'] <= 14.454), 'Fare'] = 1
data_train.loc[(data_train['Fare'] > 14.454) & (data_train['Fare'] <= 31), 'Fare']   = 2
data_train.loc[ data_train['Fare'] > 31, 'Fare'] = 3
data_train['Fare'] = data_train['Fare'].astype(int)
data_train
Out[135]:
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 0 22 1 0 0 0
1 1 1 1 38 1 0 0 1
2 1 3 1 26 0 0 0 0
3 1 1 1 35 1 0 0 0
4 0 3 0 35 0 0 0 0
5 0 3 0 25 0 0 0 2
6 0 1 0 54 0 0 0 0
7 0 3 0 2 3 1 0 0
8 1 3 1 27 0 2 0 0
9 1 2 1 14 1 0 0 1
10 1 3 1 4 1 1 0 0
11 1 1 1 58 0 0 0 0
12 0 3 0 20 0 0 0 0
13 0 3 0 39 1 5 0 0
14 0 3 1 14 0 0 0 0
15 1 2 1 55 0 0 0 0
16 0 3 0 2 4 1 0 2
17 1 2 0 30 0 0 0 0
18 0 3 1 31 1 0 0 0
19 1 3 1 21 0 0 0 1
20 0 2 0 35 0 0 0 0
21 1 2 0 34 0 0 0 0
22 1 3 1 15 0 0 0 2
23 1 1 0 28 0 0 0 0
24 0 3 1 8 3 1 0 0
25 1 3 1 38 1 5 0 0
26 0 3 0 25 0 0 0 1
27 0 1 0 19 3 2 0 0
28 1 3 1 21 0 0 0 2
29 0 3 0 25 0 0 0 0
... ... ... ... ... ... ... ... ...
861 0 2 0 21 1 0 0 0
862 1 1 1 48 0 0 0 0
863 0 3 1 21 8 2 0 0
864 0 2 0 24 0 0 0 0
865 1 2 1 42 0 0 0 0
866 1 2 1 27 1 0 0 1
867 0 1 0 31 0 0 0 0
868 0 3 0 25 0 0 0 0
869 1 3 0 4 1 1 0 0
870 0 3 0 26 0 0 0 0
871 1 1 1 47 1 1 0 0
872 0 1 0 33 0 0 0 0
873 0 3 0 47 0 0 0 0
874 1 2 1 28 1 0 0 1
875 1 3 1 15 0 0 0 1
876 0 3 0 20 0 0 0 0
877 0 3 0 19 0 0 0 0
878 0 3 0 25 0 0 0 0
879 1 1 1 56 0 1 0 1
880 1 2 1 25 0 1 0 0
881 0 3 0 33 0 0 0 0
882 0 3 1 22 0 0 0 0
883 0 2 0 28 0 0 0 0
884 0 3 0 25 0 0 0 0
885 0 3 1 39 0 5 0 2
886 0 2 0 27 0 0 0 0
887 1 1 1 19 0 0 0 0
888 0 3 1 21 1 2 0 0
889 1 1 0 26 0 0 0 1
890 0 3 0 32 0 0 0 2

891 rows × 8 columns

In [138]:
data_test[ data_test['Fare'].isnull() ][['Sex','Pclass','Age','Fare']].head()
Out[138]:
Sex Pclass Age Fare
152 0 3 60 NaN
In [139]:
data_test['Fare'].fillna(data_test['Fare'].dropna().median(), inplace=True)
data_test.loc[ data_test['Fare'] <= 7.91, 'Fare'] = 0
data_test.loc[(data_test['Fare'] > 7.91) & (data_test['Fare'] <= 14.454), 'Fare'] = 1
data_test.loc[(data_test['Fare'] > 14.454) & (data_test['Fare'] <= 31), 'Fare']   = 2
data_test.loc[ data_test['Fare'] > 31, 'Fare'] = 3
data_test['Fare'] = data_test['Fare'].astype(int)
data_test
Out[139]:
PassengerId Pclass Sex Age SibSp Parch Fare Embarked
0 892 3 0 34 0 0 0 2
1 893 3 1 47 1 0 0 0
2 894 2 0 62 0 0 0 2
3 895 3 0 27 0 0 0 0
4 896 3 1 22 1 1 0 0
5 897 3 0 14 0 0 0 0
6 898 3 1 30 0 0 0 2
7 899 2 0 26 1 1 0 0
8 900 3 1 18 0 0 0 1
9 901 3 0 21 2 0 0 0
10 902 3 0 24 0 0 0 0
11 903 1 0 46 0 0 0 0
12 904 1 1 23 1 0 0 0
13 905 2 0 63 1 0 0 0
14 906 1 1 47 1 0 0 0
15 907 2 1 24 1 0 0 1
16 908 2 0 35 0 0 0 2
17 909 3 0 21 0 0 0 1
18 910 3 1 27 1 0 0 0
19 911 3 1 45 0 0 0 1
20 912 1 0 55 1 0 0 1
21 913 3 0 9 0 1 0 0
22 914 1 1 41 0 0 0 0
23 915 1 0 21 0 1 0 1
24 916 1 1 48 1 3 0 1
25 917 3 0 50 1 0 0 0
26 918 1 1 22 0 1 0 1
27 919 3 0 22 0 0 0 1
28 920 1 0 41 0 0 0 0
29 921 3 0 24 2 0 0 1
... ... ... ... ... ... ... ... ...
388 1280 3 0 21 0 0 0 2
389 1281 3 0 6 3 1 0 0
390 1282 1 0 23 0 0 0 0
391 1283 1 1 51 0 1 0 0
392 1284 3 0 13 0 2 0 0
393 1285 2 0 47 0 0 0 0
394 1286 3 0 29 3 1 0 0
395 1287 1 1 18 1 0 0 0
396 1288 3 0 24 0 0 0 2
397 1289 1 1 48 1 1 0 1
398 1290 3 0 22 0 0 0 0
399 1291 3 0 31 0 0 0 2
400 1292 1 1 30 0 0 0 0
401 1293 2 0 38 1 0 0 0
402 1294 1 1 22 0 1 0 1
403 1295 1 0 17 0 0 0 0
404 1296 1 0 43 1 0 0 1
405 1297 2 0 20 0 0 0 1
406 1298 2 0 23 1 0 0 0
407 1299 1 0 50 1 1 0 1
408 1300 3 1 22 0 0 0 2
409 1301 3 1 3 1 1 0 0
410 1302 3 1 22 0 0 0 2
411 1303 1 1 37 1 0 0 2
412 1304 3 1 28 0 0 0 0
413 1305 3 0 24 0 0 0 0
414 1306 1 1 39 0 0 0 1
415 1307 3 0 38 0 0 0 0
416 1308 3 0 24 0 0 0 0
417 1309 3 0 24 1 1 0 1

418 rows × 8 columns

In [140]:
X_train = data_train.drop("Survived", axis=1)
Y_train = data_train["Survived"]
X_test  = data_test.drop("PassengerId", axis=1).copy()
In [158]:
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB

# Logistic Regression

logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
acc_logreg = logreg.score(X_train, Y_train)
acc_logreg
Out[158]:
0.81032547699214363
In [154]:
svc = SVC()
svc.fit(X_train, Y_train)
Y_pred = svc.predict(X_test)
acc_svc = svc.score(X_train, Y_train)
acc_svc
Out[154]:
0.85746352413019078
In [155]:
knn = KNeighborsClassifier(n_neighbors = 3)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
# acc_knn = round(knn.score(X_train, Y_train) * 100, 2)
acc_knn = knn.score(X_train, Y_train)
acc_knn
Out[155]:
0.86195286195286192
In [156]:
gaussian = GaussianNB()
gaussian.fit(X_train, Y_train)
Y_pred = gaussian.predict(X_test)
acc_gaussian = gaussian.score(X_train, Y_train)
acc_gaussian
Out[156]:
0.78114478114478114
In [159]:
linear_svc = LinearSVC()
linear_svc.fit(X_train, Y_train)
Y_pred = linear_svc.predict(X_test)
acc_linear_svc = linear_svc.score(X_train, Y_train)
acc_linear_svc
Out[159]:
0.70033670033670037
In [157]:
from sklearn.tree import DecisionTreeClassifier
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, Y_train)
Y_pred = decision_tree.predict(X_test)
acc_decision_tree = decision_tree.score(X_train, Y_train)
acc_decision_tree
Out[157]:
0.93265993265993263
In [167]:
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)
Y_rf_pred = random_forest.predict(X_test)
acc_random_forest = random_forest.score(X_train, Y_train)
acc_random_forest
Out[167]:
0.93265993265993263
In [168]:
models = pd.DataFrame({
    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', 
              'Random Forest', 'Naive Bayes', 'Linear SVC',  'Decision Tree'],
    'Score': [acc_svc, acc_knn, acc_logreg, 
              acc_random_forest, acc_gaussian,  
              acc_linear_svc, acc_decision_tree]})
models.sort_values(by='Score', ascending=False)
Out[168]:
Model Score
3 Random Forest 0.932660
6 Decision Tree 0.932660
1 KNN 0.861953
0 Support Vector Machines 0.857464
2 Logistic Regression 0.810325
4 Naive Bayes 0.781145
5 Linear SVC 0.700337
In [169]:
submission = pd.DataFrame({
        "PassengerId": data_test["PassengerId"],
        "Survived": Y_rf_pred
    })
In [170]:
submission.to_csv('submission.csv', index=False)
In [ ]: